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Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training
[article]
2021
arXiv
pre-print
Representation learning models for Knowledge Graphs (KG) have proven to be effective in encoding structural information and performing reasoning over KGs. In this paper, we propose a novel pre-training-then-fine-tuning framework for knowledge graph representation learning, in which a KG model is firstly pre-trained with triple classification task, followed by discriminative fine-tuning on specific downstream tasks such as entity type prediction and entity alignment. Drawing on the general ideas
arXiv:2112.04087v1
fatcat:h7rsdkvslzesznahcg33doii6q